Introduction and aimsCeliac disease (CD) is an autoimmune enteropathy that develops in genetically susceptible individuals. The typical gastrointestinal manifestation is diarrhea but symptoms of dyspepsia, such as epigastric pain, nausea, or satiety, can sometimes appear. Previous studies have reported that the prevalence of CD in patients with dyspepsia can be as high as 7%. The aim of the present study was to evaluate CD seroprevalence in subjects with dyspeptic symptoms and a control group in a Mexican population. Material and methodsA case-control study was conducted on blood donors that answered the PAGI-SYM questionnaire for dyspepsia and in whom IgA antibodies to tissue transglutaminase 2 (IgA anti-tTG2) and IgG antibodies to deamidated gliadin peptide (IgG anti-DGP) were determined. CD seroprevalence in subjects with dyspeptic symptoms and in asymptomatic subjects was compared. ResultsA total of 427 subjects (76.3% men), with a mean patient age of 34 years (range of 18-65 years) were included. Of those participants, 87 (20.3%) had symptoms of dyspepsia (group A) and 340 (79.6%) were asymptomatic (group B). Antibodies were positive in one (1.15%) of the group A subjects (1/87, 95% CI 0.2-6%), whereas they were positive in 4 (1.18%) of the group B subjects (4/340, 95% CI 0.4-2.9%, p = 0.59). ConclusionsCD seroprevalence in the study population with dyspeptic symptoms (1%) was not different from that of the control population. Thus, CD screening in Mexican patients with dyspepsia is not justified.

Digital forensics is the scientific acquisition, analysis, and preservation of data contained in electronic media. It is the most intricate step of the cybercrime investigation process often used as the evidence that can be presented in the court of law for most prosecutable cases. This article briefs about one of the branches of Digital forensics which is Mobile forensics that aims to recover digital evidence or relevant data from a mobile device in a way that will preserve evidence. This article aims to provide the review of state-of-art and the brief about the forensic lifecycle, process and methodologies that are used for extracting and preserving the data from Mobile phones or smart phones and also some of the tools and software’s.

The important penetration of distributed energy resources into the power grid comes with a raft of challenges for urban distribution management. Knowing the grid topology in real-time is essential to ensure the reliability of operational power flow control. However, in practice, the topology information is often unavailable. Worst, this latter can change relatively more frequently with limited line monitoring devices. To address these drawbacks, we provide a new approach relying on Bayesian network models to detect connections amongst grid buses. We prove that the grid topology estimation can be formulated as a Bayesian network structure learning. The robustness of our approach, as well as its benefits compared to other estimation algorithms, are highlighted in numerical experiments.

This report provides information about modern pipe samples containing dent and cracked gouge defects for performance evaluation of inspection technologies. Five mechanical damage dent and gouge defects were created in a realistic manner in modern pipes and then submitted to internal pressure cycling to initiate a significant crack at the gouge bottom. Three types of defects were created: shallow gouge in shallow dent, severe gouge in shallow dent and shallow gouge in severe dent. All defects are planned to be used to performance evaluation of inspection technologies. These defects are similar to those created in project PRCI MD-4-1 for destructive metallurgical characterization and for burst and fatigue tests. This version of the report contains the results of measurements of defects' dimensions, and other observations, to be held confidential, in order to allow blind testing of inspection technologies.

Accurate classification of remote sensing (RS) images is perennial topic of interest in the RS community. Recently, transfer learning, especially for fine-tuning pre-trained convolutional neural networks (CNNs), has been proposed as a feasible strategy for RS scene classification. However, because the target domain (i.e., the RS images) and the source domain (e.g., ImageNet) are quite different, simply using the model pre-trained on an ImageNet dataset presents some difficulties. The RS images and the pre-trained models need to be properly adjusted to build a better classification system. In this study, an adaptive learning strategy for transferring a CNN-based model is proposed. First, an adaptive transform is used to adjust the original size of the RS image to a certain size, which is tailored to the input of the subsequent pre-trained model. Then, an adaptive transferring model is proposed to automatically learn what knowledge from the pre-trained model should be transferred to the RS scene classification model. Finally, in combination with a label smoothing approach, adaptive label is presented to generate soft labels based on the statistics of the classification model predictions for each category, which is beneficial for learning the relationships between the target and non-target categories of scenes. In general, the proposed methods adaptively manage the input, model, and label simultaneously, which leads to better classification performance for RS scene classification. The proposed methods are tested on three widely-used data sets and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods.

Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budget for this task is usually limited. To reduce the reliance on labeled samples, deep semi-supervised learning (SSL), which jointly learns from labeled and unlabeled samples, has been introduced in the literature. However, learning robust and discriminative features from unlabeled data is a challenging task due to various noise effects and ambiguity of unlabeled samples. As a result, recent advances are constrained, mainly in the pre-training or warm-up stage. In this paper, we propose a deep probabilistic framework to generate reliable pseudo labels to explicitly learn discriminative features from unlabeled samples. The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity. Our proposed framework takes only 10 labeled samples per class to represent the label set as an uncertainty-aware distribution in the latent space. The pseudo labels are then generated for those unlabeled samples whose feature values match the distribution with high probability. By performing extensive experiments on four publicly available datasets, we show that our framework can generate reliable pseudo labels to significantly improve the generalization capacity of several state-of-the-art DNNs. In addition, we introduce a new DNN for HSI classification that demonstrates outstanding accuracy results in comparison with its rivals.

The use of optical fiber sensors to monitor electric machines is reviewed. A specific application of a new multiparametric transducer to measure dynamic strain and temperature directly in the stator core of an induction motor is presented.

This work features a highly efficient high power density dc-dc converter based on a soft switching capable, dual-active-bridge topology. Moreover, to reduce switching losses at high frequencies and consequently minimize the size of the passive components, modern 1200 V silicon carbide half bridge modules are used. Furthermore, to decrease the size of the cooling apparatus, compact 3D-printed fluid coolers are utilized. These measures that has been stated previously resulted in a highly integrated active part of the converter with a total box volume of 8 dm³ including the semiconductor switches, capacitors and sensors for a 200 kW unit. The first prototype shows a power density capability of 6.48 kW/dm³ at a maximum power output of 201 kW. The design of this converter and its advanced cooling concept as well as in-operation measurements will be presented.

Algorithmic exploitation of medical data for diagnostic purposes has become state of the art in the modern medical world. Applying artificial intelligence algorithms is gaining importance and electrocardiogram recordings have successfully been used as input for deep learning models and produce viable diagnoses. Algorithms are noninvasive, relatively low-cost and promise to have high diagnostic leverage. However, for supervised learning algorithms such as deep learning models the amount of high quality data labelled with correct diagnoses required for training is considerable. In this paper, we present a pipeline that processes raw electrocardiogram recordings preparing them for use in training and validation of neural network models. Although, the electrocardiogram is widely used, appropriately labelled training data is rare and provided in different formats and from technically different sources. Therefore, our end-to-end pipeline not only processes data from modern digital ECG devices, e.g. in XML file format, but can also extract all necessary information from PDF files (both scanned hard copies and digitally generated PDFs). We present a use case in which data from XML and PDF sources is read, cleaned and combined into a unified dataset to be used by a model predicting myocardial scar. Our pipeline will become a cornerstone of our environment for building AI based diagnostic instruments.